The present invention relates to postprocessing of digital images and, more particularly, to a method of predicting ringing artifacts in a decompressed digital image to facilitate postprocessing for mitigation of the artifacts.
Block-based, transform coding is commonly used to compress digital images. For example, block-based, transform coding is a feature of the JPEG (ISO 10918) standard for still images and the MPEG-2 (ISO 13818) standard related to motion video. Generally, block-based, transform coding involves subdividing an image into blocks of pixels; applying a reversible transform, such as the Discrete Cosine Transform (DCT), to the luminance and chrominance of the pixels of the blocks; quantizing the resulting transform coefficients; and coding the results. At the decoder, the process is reversed to reconstruct the image. While block-based transform coding can achieve high compression ratios, information in the original image is discarded during the compression process degrading the reconstructed image, especially if the image is part of a highly compressed video sequence with substantial motion.
When a transform coded image is reconstructed the image may include visible artifacts of the compression process. One common artifact of block-based transform coding is the “blocking” effect or “grid noise.” The blocking effect is the result of the approximation of the DC transform coefficient during quantization. In quantizing the transform coefficients the encoder selects one of the available quantization parameters closest in value to the coefficient to represent the coefficient. This “rounding off” can produce pixels of visually different colors in adjacent blocks even though the corresponding pixels of the original image were nearly identical. As a result, blocks of pixels created for the transform coding process may be visible in the reconstructed image. The blocking effect becomes more pronounced as the quantization steps are coarsened to reduce the data rate. The blocking effect is a particular problem along edges or color boundaries in the image or in areas of the image that are of relatively uniform color.
In addition, the reconstructed image may exhibit “staircase noise,” a term descriptive of the appearance of an edge in the reconstructed image. Staircase noise is the result of enhancement of the blocking effect for blocks that bridge an edge in the image.
A third compression artifact of reconstructed digital images is the “ringing” artifact or “mosquito noise.” Ringing produces jagged or fuzzy lines in the vicinity of sharp edges in the image. For example, text includes many sharp edges and is a prime location for ringing artifacts. Ringing noise in video is visible as local flickering near edges. Ringing is the result of noise produced by coarse quantization of higher frequency transform coefficients and the lack of correlation between pixels on either side of an edge. While the blocking effect is the predominant compression artifact in severely compressed images, ringing is typically the most visible artifact at lower compression ratios.
Compression artifacts can be annoying to viewers of the reconstructed image and digital images are commonly further processed after decompression or postprocessed to mitigate the effects of artifacts in the reconstructed image. Some postprocessing methods attempt to recover the original image from a combination of the decompressed image data and information related to the smoothness properties of the image before compression. These methods require information in addition to the information necessary to reconstruct the image requiring higher rates of data transmission. The blocking effect is often addressed by filtering the pixels at the edges of block boundaries to smooth the transition between blocks. However, filtering at the block boundaries usually has little effect on ringing artifacts which tend to lie along edges in the image, but are not transmitted across block boundaries. Filtering may be applied to edges in the image to reduce ringing artifacts, but edge detection is computationally expensive and filtering blurs the edges reducing the sharpness of the image. In general, postprocessing methods are complex, often iterative and time consuming, and can degrade the sharpness of the image. These factors limit the usefulness of many postprocessing methods as processes to be applied wholesale to decompressed images, especially in real time video applications. However, if areas of an image that are likely to produce a ringing artifact could be identified, a postprocessing method could be selectively applied to only limited areas of the image, reducing the impact on image quality and the time and computational facilities necessary for postprocessing.
What is desired, therefore, is a method of identifying areas of a reconstructed image that are likely to exhibit a ringing artifact.